this work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is ...
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ISBN:
(纸本)9783319689357;9783319689340
this work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.
Brain-Computer interface (BCI) is a new technique which allows direction connection between human and computer or other external device. It employs the classification of event-related potential to control the equipmen...
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ISBN:
(纸本)9783319689357;9783319689340
Brain-Computer interface (BCI) is a new technique which allows direction connection between human and computer or other external device. It employs the classification of event-related potential to control the equipment rather than using language or limb movement. Currently, one of the most important issues for ERP-based BCIs is that the ERP classification performance degrades when the training number of samples is small. In order to solve this problem, semi-supervised regularized discriminant analysis (SRDA) was proposed to extract features and classify ERP patterns by integrating semi-supervised learning and regularization approach. the labeled data was used to maximize the separability between different classes and calculate the within-class covariance matrix by regularization. the labeled and unlabeled data were employed to construct the penalty term by neighbor graph. Our proposed approach was evaluated on the BCI Competition Challenge dataset and the simulation results indicated that it achieved a better accuracy than the traditional algorithms.
Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. they show how some distinct choice of human behavior or preference influences a decision. ...
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this study uses data collected at two National Summer Transportation Institute (NSTI) programs in Connecticut and Mississippi to investigate high school students' perceptions and preferences about education in sci...
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ISBN:
(纸本)9781941763650
this study uses data collected at two National Summer Transportation Institute (NSTI) programs in Connecticut and Mississippi to investigate high school students' perceptions and preferences about education in science, technology, engineering and mathematics (STEM). Family background has a significant impact on a high school student's interest in STEM, as shown during the student recruitment stage and by the analysis of the students' college education plans prepared upon graduation from the two NSTI programs. the building exercise and competition instrument is the most effective among the few examined, while passive learning is not what young people prefer when briefly introduced in the two NSTI programs.
SSL/TLS protocol is widely used for secure web applications (i.e., HTTPS). Classifying encrypted SSL/TLS based applications is an important but challenging task for network management. Traditional traffic classificati...
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ISBN:
(纸本)9781509049066
SSL/TLS protocol is widely used for secure web applications (i.e., HTTPS). Classifying encrypted SSL/TLS based applications is an important but challenging task for network management. Traditional traffic classification methods are incapable of accomplishing this task. Several recently proposed approaches that focused on discriminating defining fingerprints among various SSL/TLS applications have also shown various limitations. In this paper, we design a Weighted ENsemble Classifier (WENC) to tackle these limitations. WENC studies the characteristics of various sub-flows during the HTTPS handshake process and the following data transmission period. To increase the fingerprint recognizability, we propose to establish a second order Markov chain model with a fingerprint variable jointly considering the packet length and the message type during the process of HTTPS handshake. Furthermore, the series of the packet lengths of application data is modeled as HMM with optimal emission probability. Finally, a weighted ensemble strategy is devised to accommodate the advantages of several approaches as a unified one. Experimental results show that the classification accuracy of the proposed method reaches 90%, with an 11% improvement on average comparing to the state-of-the-art methods.
the proceedings contain 44 papers. the topics discussed include: a learning material for physics experiment with high-accuracy using computer vision technique;automatic classification of remarks in werewolf BBS;a rapi...
ISBN:
(纸本)9781538633021
the proceedings contain 44 papers. the topics discussed include: a learning material for physics experiment with high-accuracy using computer vision technique;automatic classification of remarks in werewolf BBS;a rapid incremental frequent pattern mining algorithm for uncertain data;global and local bursts detection in streaming data;two-mode three-way dominance points model for periodic dissimilarity;an intelligent noninvasive taste detection app for watermelons;automated risk identification of CMMI project planning using ontology;and depth recognition in 3D translucent stereoscopic imaging of medical volumes by means of a glasses-free 3d display.
the widely using CMOS technology implementing with irreversible logic will hit a scaling limit beyond 2020 and the major limiting factor is increased power dissipation. the irreversible logic is replaced by reversible...
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the paper presents the system architecture, development and prototype implementation of a new integrated system for simulation of automated industrial processes using advanced technologies, in accordance with CPS/Indu...
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ISBN:
(纸本)9781538606971
the paper presents the system architecture, development and prototype implementation of a new integrated system for simulation of automated industrial processes using advanced technologies, in accordance with CPS/Industry 4.0 principles. the need to develop such a system is underscored by the interest of the educational stakeholders: students, faculty members, high-level industry partners, for an educational system to be used in specialized training in interdisciplinary areas including edge systems in terms of modern control technologies, open systems available to be reconfigured upon request with other technologies and mission-critical applications for different process engineering fields such as key energy applications. the process simulator can be operated in educational environments as follows: as didactic equipment for learning and deepening PLCs programming languages skills;equipment for testing various complex scenarios of some energy processes while allowing on-demand reconfiguration for some industrial systems which cannot be implemented in reality due to the high cost and/or the operational safety. Given its properties flexibility, interoperability, open architecture, compatibility in communication, friendly human-machine interface and industrial applications, ASID is able to cover a vast array of teaching scenarios as well as serve as a platform for control algorithm implementation and testing. the modular configuration allows for increased versatility and flexibility and the long-term competitiveness of the final demonstrator is assured through hardware and software updates.
Ranking score plays an important role in the system of content-based retrieval. Given a query, the database items are ranked according to the ranking scores in a descending order, and the top-ranked items are returned...
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ISBN:
(数字)9783319689357
ISBN:
(纸本)9783319689357;9783319689340
Ranking score plays an important role in the system of content-based retrieval. Given a query, the database items are ranked according to the ranking scores in a descending order, and the top-ranked items are returned as retrieval results. In this paper, we propose a new ranking scoring function based on the convolutional neural network (CNN). the ranking scoring function has a structure of CNN, and its parameters are adjusted to both queries and query preferences. the learning process guarantees that the ranking score of the query itself is large, and also the ranking scores of the positives (database items which the query wants to link) are larger than those of the negatives (database items which the query wants to avoid). Moreover, we also impose that the neighboring database items have similar ranking scores. An optimization problem is formulated and solved by Estimation-Maximization method. Experiments over the benchmark data sets show the advantage over the existing learning-to-rank methods.
作者:
Goceri, EvginGoceri, NumanAkdeniz University
Engineering Faculty Computer Engineering Department Dumlupinar Boulevard Antalya07058 Turkey Evosoft GmbH
Business Function Information Technology Solutions Marienbergstr. 78-80 Nuremberg90411 Germany
Deep learning (DL) methods are a set of algorithms in Machine learning (ML), which provides an effective way to analysis medical images automatically for diagnosis/assessment of a disease. DL enables higher level of a...
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